Showing papers in "International Journal of Pervasive Computing and Communications in 2020"

Trusted Secure Geographic Routing Protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol

Abstract: Purpose This study aims to evaluate the direct trust value for each node and calculate the trust value of all nodes satisfying the condition and update the trust value and value each trust update interval for a secure and efficient communication between sender and destination node. Hence, a Trusted Secure Geographic Routing Protocol (TSGRP) has been proposed for detecting attackers (presence of the hacker), considering the trust value for a node produced by combining the location trusted information and the direct trusted information. Design/methodology/approach Amelioration in the research studies related to mobile ad hoc networks (MANETs) and wireless sensor networks has shown greater concern in the presence of malicious nodes, due to which the delivery percentage in any given network can degrade to a larger extent, and hence make the network less reliable and more vulnerable to security. Findings TSGRP has outperformed the conventional protocols for detecting attacks in MANET. TSGRP is establishing a trust-based secure communication between the sender and destination node. The evaluated direct trust value is used after the transmission of route-request and route-reply packets, to evaluate the direct trust value of each node and a secure path is established between the sender and the destination node. The effectiveness of the proposed TSGRP is evaluated through NS-2 simulation. Originality/value The simulation results show the delay of the proposed method is 92% less than PRISM approach and the overhead of the proposed TSGRP approach is 61% less than PRISM approach.

Continuance adoption of mobile-based payments in Covid-19 context: an integrated framework of health belief model and expectation confirmation model

Abstract: Shifting to mobile-based banking transactions from physical banking transactions can be considered as a social distancing mechanism, which helps to prevent the spread of Covid-19 virus. As the spread of Covid-19 is expected to continue for long, the continued usage of mobile-based payment services as a strategy to maintain social distancing has to prevail. Hence, this study aims to propose an integrated framework of mobile payments adoption and its continuance intention by integrating health belief model (HBM) and expectation confirmation model (ECM) of information system continuance.,The subject of the study constitutes new adopters of mobile payments. A total of 654 respondents participated in the survey. The conceptual model was empirically validated using structural equation modeling and serial mediation analysis.,The study found that the HBM constructs, namely, perceived severity, perceived susceptibility and self-efficacy significantly influenced adoption/confirmation of mobile-based payment services. The continuance intention was significantly predicted by perceived usefulness and perceived satisfaction. Furthermore, the perceived health threat (comprising perceived severity and perceived susceptibility) indirectly affects continuance intention through confirmation, perceived usefulness and satisfaction.,There are short-term and long-term implications for the study. Short-term implications include triggering the HBM at policy levels, to adopt mobile payments/banking as a means of social distancing in the wake of the increasing threat of Covid-19 in India. Long-term implication for service providers is to convert adopters into loyal consumers by enhancing usefulness and satisfaction.,The study proposes a novel attempt to explain the adoption and continuance of mobile-based payment as a preventive health behavior to contain the spread of Covid-19 outbreak. The study proposes an integrated framework of HBM and ECM to explain pre-adoption and post-adoption behavior of consumers with respect to mobile-based payment services during Covid-19 context.

Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors

Abstract: Purpose Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors Design/methodology/approach In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government Findings COVID-19 patients are either symptomatic or asymptotic Symptomatic patients have symptoms such as fever, cough and difficulty in breathing But patients are also asymptotic, which is very difficult to detect and monitor by isolating them Originality/value Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading

Social economic impact of COVID-19 outbreak in India

Abstract: The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the internet of things (IoT) market. To overcome all the above issues, IoT devices and sensors can be used to track and monitor the movement of the people, so that necessary actions can be taken to prevent the spread of coronavirus disease (COVID-19). Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition.,To respond to the global COVID-19 outbreak, the social-economic implications of COVID-19 on specific dimensions of the global economy are analyzed in this study. The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the IoT market. To overcome these issues IoT devices and sensors can be used to track and monitor the movement of the people so that necessary actions can be taken to prevent the spread of COVID-19. Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition. A few reviews, approaches, and guidelines are provided in this article along these lines. Moreover, insights about the effects of the pandemic on various sectors such as agriculture, medical industry, finance, information technology, manufacturing and many others are provided. These insights may support strategic decision making and policy framing activities for the top level management in private and government sectors.,With insecurities of a new recession and economic crisis, key moments such as these call for strong and powerful governance in health, business, government, and large society. Instant support measures have to be initiated and adapted for those who can drop through the cracks. Mid- and long-term strategies are required to stabilize and motivate the economy during this recession.,A comprehensive social-economic development strategy that consists of sector by sector schemes and infrastructure that supports business to ensure the success of those with reliable and sustainable business models is necessary. From the literature analysis and real world observations it is concluded that the IoT, sensors, wearable devices and computational technologies plays major role in preserving the economy of the country by preventing the spread of COVID-19.

Applying deep learning algorithm to maintain social distance in public place through drone technology

Abstract: The purpose of this paper is to inspect whether the people in a public place maintain social distancing. It also checks whether every individual is wearing face mask. If both are not done, the drone sends alarm signal to nearby police station and also give alarm to the public. In addition, it also carries masks and drop them to the needed people. Nearby, traffic police will also be identified and deliver water packet and mask to them if needed.,The proposed system uses an automated drone which is used to perform the inspection process. First, the drone is being constructed by considering the parameters such as components selection, payload calculation and then assembling the drone components and connecting the drone with the mission planner software for calibrating the drone for its stability. The trained yolov3 algorithm with the custom data set is being embedded in the drone’s camera. The drone camera runs the yolov3 algorithm and detects the social distance is maintained or not and whether the people in public is wearing masks. This process is carried out by the drone automatically.,The proposed system delivers masks to people who are not wearing masks and tells importance of masks and social distancing. Thus, this proposed system would work in an efficient manner after the lockdown period ends and helps in easy social distance inspection in an automatic manner. The algorithm can be embedded in public cameras and then details can be fetched to the camera unit same as the drone unit which receives details from the drone location details and store it in database. Thus, the proposed system favours the society by saving time and helps in lowering the spread of corona virus.,It can be implemented practically after lockdown to inspect people in public gatherings, shopping malls, etc.,Automated inspection reduces manpower to inspect the public and also can be used in any place.,This is the original project done with the help of under graduate students of third year B.E. CSE. The system was tested and validated for accuracy with real data.

Pervasive computing in the context of COVID-19 prediction with AI-based algorithms

Abstract: Purpose: The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters Design/methodology/approach: For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed Data-imputation is carried out by principal component analysis A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease Findings: Machine learning models are very efficient in predicting diseases In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms A mathematical model SIR is integrated with machine learning techniques for better classification Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history Originality/value: The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model © 2020, Emerald Publishing Limited

In defence of digital contact-tracing: human rights, South Korea and Covid-19

Abstract: Purpose The media has even been very critical of some East Asian countries' use of digital contact-tracing to control Covid-19 For example, South Korea has been criticised for its use of privacy-infringing digital contact-tracing However, whether their type of digital contact-tracing was unnecessarily harmful to the human rights of Korean citizens is open for debate The purpose of this paper is to examine this criticism to see if Korea's digital contact-tracing is ethically justifiable Design/methodology/approach This paper will evaluate Korea's digital contact-tracing through the lens of the four human rights principles to determine if their response is ethically justifiable These four principles were originally outlined in the European Court of Human Rights, namely, necessary, proportional, scientifically valid and time-bounded (European Court of Human Rights 1950) Findings The paper will propose that while the use of Korea's digital contact-tracing was scientifically valid and proportionate (albeit, in need for improvements), it meets the necessity requirement, but is too vague to meet the time-boundedness requirement Originality/value The Covid-19 pandemic has proven to be one of the worst threats to human health and the global economy in the past century There have been many different strategies to tackle the pandemic, from somewhat laissez-faire approaches, herd immunity, to strict draconian measures Analysis of the approaches taken in the response to the pandemic is of high scientific value and this paper is one of the first to critically engage with one of these methods - digital contact-tracing in South Korea

A case-study to examine doctors’ intentions to use IoT healthcare devices in Iraq during COVID-19 pandemic

Abstract: Purpose: Several countries have been using internet of things (IoT) devices in the healthcare sector to combat COVID-19 Therefore, this study aims to examine the doctors’ intentions to use IoT healthcare devices in Iraq during the COVID-19 pandemic Design/methodology/approach: This study proposed a model based on the integration of the innovation diffusion theory (IDT) This included compatibility, trialability and image and a set of exogenous factors such as computer self-efficacy, privacy and cost into the technology acceptance model comprising perceived ease of use, perceived usefulness, attitude and behavioral intention to use Findings: The findings revealed that compatibility and image of the IDT factors, have a significant impact on the perceived ease of use, perceived usefulness and behavioral intention, but trialability has a significant impact on perceived ease of use, perceived usefulness and insignificant impact on behavioral intention Additionally, external factors such as privacy and cost significantly impacted doctors’ behavioral intention to use Moreover, doctors’ computer self-efficacy significantly influenced the perceived ease of use, perceived usefulness and behavioral intention to use Furthermore, perceived ease of use has a significant impact on perceived usefulness and attitude, perceived usefulness has a significant impact on attitude, which, in turn, significantly impacting doctors' behavior toward an intention to use Research limitations/implications: The limitations of the present study are the retractions of the number of participants and the lack of qualitative methods Originality/value: The finding of this study could benefit researchers, doctors and policymakers in the adaption of IoT technologies in the health sectors, especially in developing counties © 2020, Emerald Publishing Limited

Analysing the impact of COVID-19 on over-the-top media platforms in India

Abstract: The outbreak of COVID-19 saw a robust increase in viewership of over-the-top (OTT) media platforms. This study aims to investigate the impact of COVID-19 on OTT platforms in India, as it has led to reshaping consumer content preferences.,The authors have conducted primary research by doing a survey and focus group discussion. The first study has focused on the impact of various factors such as time, content, convenience, satisfaction and work from home (WFH) on OTT platforms during the COVID-19 crisis and the second study has focused on change in behavior of people before and during lockdown using visual representation.,The findings of this study show that lockdown has played a major role in the increase in viewership of OTT platforms, as people working from home are also using OTT platforms more. The average hours spent on OTT have increased from 0–2 to 2–5 h and average spending that users are willing to make on OTT platforms is Rs 100–300 (per month). The satisfaction level of customers is directly related to space to watch with family, time to use OTT platforms, the quality of content on OTT platforms and preference of OTT platform over television. Also, factors such as age group, occupation, city and income groups also determine the usage of the OTT platform.,The main contribution of this paper is to analyze the customer needs that impact their satisfaction level.

Smart epidemic tunnel: IoT-based sensor-fusion assistive technology for COVID-19 disinfection

Abstract: Purpose: The purpose of the presented IoT based sensor-fusion assistive technology for COVID-19 disinfection termed as “Smart epidemic tunnel” is to protect an individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on human motion detection Design/methodology/approach: The presented research work discusses a smart epidemic tunnel that can assist an individual in immediate disinfection from COVID-19 infections The authors have presented a sensor-fusion-based automatic sanitizer tunnel that detects a human using an ultrasonic sensor from the height of 1 5 feet and disinfects him/her using the spread of a sanitizer spray The presented smart tunnel operates using a solar cell during the day time and switched to a solar power-bank power mode during night timings using a light-dependent register sensing unit Findings: The investigation results validate the performance evaluation of the presented smart epidemic tunnel mechanism The presented smart tunnel can prevent or disinfect an outsider who is entering a particular building or a premise from COVID-19 infection possibilities Furthermore, it has also been observed that the presented sensor-fusion-based mechanism can disinfect a person in a time of span of just 10 s The presented smart epidemic tunnel is embedded with an intelligent sanitizer sensing unit which stores the essential information in a cloud platform such as Google Fire-base Thus, the proposed system favours society by saving time and helps in lowering the spread of coronavirus It also provides daily, weekly and monthly reports of the counts of individuals, along with in-out timestamps and power usage reports Practical implications: The presented system has been designed and developed after the lock-down period to disinfect an individual from the possibility of COVID-19 infections Social implications: The presented smart epidemic tunnel reduced the possibility by disinfecting an outside individual/COVID-19 suspect from spreading the COVID-19 infections in a particular building or a premise Originality/value: The presented system is an original work done by all the authors which have been installed at the Symbiosis Institute of Technology premise and have undergone rigorous experimentation and testing by the authors and end-users © 2020, Emerald Publishing Limited

Mobile technologies for contact tracing and prevention of COVID-19 positive cases: a cross- sectional study

Abstract: Purpose: The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread Design/methodology/approach: COVID-19 pandemic is the utmost global economic and health challenge of the century One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation Findings: Although it takes the information from various clients, there are numerous odds in the information The sincere measures were taken by the authors to avoid the abuse of information by any kind A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody Originality/value: This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information © 2020, Emerald Publishing Limited

Pediatric and geriatric immunity network mobile computational model for COVID-19

Abstract: The computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients; moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled.,In this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India.,This study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient.,COVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.

SARS n-CoV2-19 detection from chest x-ray images using deep neural networks

Abstract: Purpose: Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Design/methodology/approach: The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection. Findings: The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods. Originality/value: The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.

Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups

Abstract: Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts using physiological measurements achieved promising results. Nevertheless, existing systems have some limitations that hinder their use in vehicles. To overcome these limitations, this paper aims to investigate the development of a low-cost, non-invasive drowsiness detection system, using physiological signals obtained from conventional wearable devices.,Two simulator studies, the first study in a low-level driving simulator (N = 10) to check feasibility and efficiency, and the second study in a high-fidelity driving simulator (N = 30) including two age groups, were conducted. An algorithm was developed to extract features from the heart rate signals and a data set was created by labelling these features according to the identified driver state in the simulator study. Using this data set, binary classifiers were trained and tested using various machine learning algorithms.,The trained classifiers reached a classification accuracy of 99.9%, which is similar to the results obtained by the studies which used intrusive electrodes to detect ECG. The results revealed that heart rate patterns are sensitive to the drivers’ age, i.e. models trained with data from one age group are not efficient in detecting drowsiness for another age group, suggesting to develop universal driver models with data from different age groups combined with individual driver models.,This work investigated the feasibility of driver drowsiness detection by solely using physiological data from wrist-worn wearable devices, such as smartwatches or fitness trackers that are readily available in the consumer market. It was found that such devices are reliable in drowsiness detection.

Mobile application based speech and voice analysis for COVID-19 detection using computational audit techniques

Abstract: Purpose: It has been six months from the time the first case was registered, and nations are still working on counter steering regulations The proposed model in the paper encompasses a novel methodology to equip systems with artificial intelligence and computational audition techniques over voice recognition for detecting the symptoms Regular and irregular speech/voice patterns are recognized using in-built tools and devices on a hand-held device Phenomenal patterns can be contextually varied among normal and presence of asymptotic symptoms Design/methodology/approach: The lives of patients and healthy beings are seriously affected with various precautionary measures and social distancing The spread of virus infection is mitigated with necessary actions by governments and nations Resulting in increased death ratio, the novel coronavirus is certainly a serious pandemic which spreads with unhygienic practices and contact with air-borne droplets of infected patients With minimal measures to detect the symptoms from the early onset and the rise of asymptotic outcomes, coronavirus becomes even difficult for detection and diagnosis Findings: A number of significant parameters are considered for the analysis, and they are dry cough, wet cough, sneezing, speech under a blocked nose or cold, sleeplessness, pain in chests, eating behaviours and other potential cases of the disease Risk- and symptom-based measurements are imposed to deliver a symptom subsiding diagnosis plan Monitoring and tracking down the symptoms inflicted areas, social distancing and its outcomes, treatments, planning and delivery of healthy food intake, immunity improvement measures are other areas of potential guidelines to mitigate the disease Originality/value: This paper also lists the challenges in actual scenarios for a solution to work satisfactorily Emphasizing on the early detection of symptoms, this work highlights the importance of such a mechanism in the absence of medication or vaccine and demand for large-scale screening A mobile and ubiquitous application is definitely a useful measure of alerting the officials to take necessary actions by eliminating the expensive modes of tests and medical investigations © 2020, Emerald Publishing Limited

Tasks mapping in the network on a chip using an improved optimization algorithm

Abstract: A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.,In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.,The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.,As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

Monitoring and sensing COVID-19 symptoms as a precaution using electronic wearable devices

Abstract: The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.,The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.,Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.,The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.

IoT role in prevention of COVID-19 and health care workforces behavioural intention in India-an empirical examination

Abstract: This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19.,IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19.,In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a “distinct advantage innovation,” which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services.,The contemporary exploration study focuses on the proposed IoT system for the treatment of patients in this progressing COVID-19. The working principle of IoT approach incorporates the mix of human services apparatuses, clinical treatment framework, Web organize, programming and administrations. IoT framework empowers the information assortment, report observing, understanding database, testing pictures and investigation and so forth. Data has been collected through online mode; in this study, the authors adopted empirical research design. Total 150 (118/150 = 78.66% respondent response ratio) online questionnaires were sent in the Chennai city of Tamilnadu, India. The participated nature of work is clinical examination in critical care division.

Detecting coronavirus contact using internet of things

Abstract: Purpose The purpose of this paper is to identify coronavirus contact using internet of things The disease is said to be highly contagious with the contact of infected persons Feared to be air-borne, droplets of body fluids can transmit the disease in a matter of hours The predominant symptoms of the COVID-19 are high fever, cough, breathing problem, etc Recent studies have demonstrated the evolution of the disease to hide its symptoms As it is highly transmissible, this disease might spread at an exponential rate costing the lives of thousands of people The chain of transmission has to be detected with utmost priority through early detection and isolation of infected people Automated internet of things (IoT) devices can be used in design and implementation of a prediction scheme for reporting the health-care risks of the patients with various parameters such as temperature, humidity and blood pressure Design/methodology/approach IoT is a configuration of multiple autonomous and embedded wireless devices for serving a purpose Every object possesses an individual identity and will serve to register critical events as entries for future learning and decisions IoT plays an inevitable role in medical industries, detection of vital signs of diseases and monitoring Among other life-threatening diseases, a new pandemic is on rise among world nations COVID-19, a novel severe acute respiratory syndrome virus originated from animals in December 2019 and is becoming a serious menace to Governments, despite serious measures of lockdowns Findings In this paper, the authors defined an architecture of an IoT system to predict the Covid-19 disease by getting the data from the human through sensors and send the data to the doctor using mobile, computer, etc The main goal is early health surveillance by predicting COVID-19 Accordingly, the authors are able to identify both symptomatic and asymptomatic patients, which will help in the early prediction of disease Originality/value Using the proposed method, the authors can save the time of both patient and doctor by ensuring timely medical treatment and contribute toward breaking the transmission chain In so doing, the method also contributes toward avoiding unnecessary expenses and saving human lives

Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach

Abstract: According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease.,A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases.,The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6.,The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

General public's attitude toward governments implementing digital contact tracing to curb COVID-19-a study based on natural language processing

Abstract: Purpose Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public’s emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing. Design/methodology/approach For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing. Findings This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government. Originality/value Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors’ knowledge, this is the first study conducted with an objective of understanding the general public’s perception of contact tracing.

How optimistic do citizens feel about digital contact tracing? – Perspectives from developing countries

Abstract: Purpose Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing. Design/methodology/approach For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing. Findings The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing. Originality/value After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.

Assessing nitrogen dioxide (NO2) impact on health pre- and post-COVID-19 pandemic using IoT in India

Abstract: Purpose - Corona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2;it affects the respiratory system of the human and sometimes leads to death The COVID-19 mainly attacks the person with previous lung diseases;the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19 Design/methodology/approach - Spatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data Moreover, aerosol optical depth analysis has been carried out based on NASA's Earth Observing System data Findings - The results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2 Originality/value - Using the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body

Tracking the Covid zones through geo-fencing technique

Abstract: Because of the outbreak of Covid 19, the entire world is thinking of new strategies, preventive measures to safeguard the human life from the widespread of the pandemic. The areas where people are affected are marked as containment zones and people are not allowed to exit out of those areas. Similarly, new people are not allowed to enter inside those areas. Hence, the purpose of this paper is to propose a methodology to track the Covid zones, to enhance and tighten the security measures. A geo-fence is created for the containment zone. The person who enters or exits out of that particular zone will be monitored and alert message will be sent to that person’s mobile.,After tracking the location of a suspicious individual, the geo-fenced layer is mapped in the area and then the virtual perimeter is used for further trapping process. This geo-fenced layer can be viewed by the citizens as soon as it is updated by the Covid monitoring team. The geo-fencing is a concept of building a virtual perimeter area. This virtual perimeter monitoring system helps in monitoring the containment zones effectively. It reduces operational costs by using an automated system based on wireless infrastructure. It also alerts the authorities immediately to catch the violators. Thus, it helps to speed up the process of inspecting the containment zones and monitoring the individuals who violate the rules given by government.,The proposed methodologies will be an effective way to track the Covid’s communal spread. But the workflow of the system demands the required data sets and permission in legal manner to set up the environment that maintains the constitutional law and order in practice. The application developed was a prototype to display how it works if the required data sets are provided by the government. There are several tracking models that are released across the world such as Aarogya setu (India), Trace together (Singapore) and Hagmen (Israel). All these models are based on Bluetooth proximity identification; though Bluetooth proximity identification is helpful for high range in a short distance, the privacy concern is debatable one. Using modern technology, it is so easy to crack the individual gadgets and with Bluetooth enabling it makes things even worse. Thus, it is important to maintain the tracking a safer and secure one, and another issue with those Bluetooth-based applications is that tracking can be done only if the user enabled the Bluetooth option, if not the entire functioning would become a mess. The proposed methodology of tracking without Bluetooth will ensure data security also.,This was developed as a project by our third-year students of the Department of Information Technology of our college.

Smart scheduling on cloud for IoT-based sprinkler irrigation

Abstract: The purpose of this paper aims to reduce the manpower, electricity, and water consumption for irrigation.,The IoT-based smart irrigation system designed with various sensors to collect farm field data, and stored all the data in the cloud for scheduling the irrigation.,This system reduces the water and electricity consumption, and labor cost.,Difficult to implement on a small farm field with different crops.,Crop type, soil type and environment data should be considered for better saving of water.,Reduces the water consumption, electricity, man power and increase production.,The soil type, crop type and environment data have been added before irrigation. The climate data also included before scheduling. Dynamic changing of irrigation timings based on the climate and sensor data.

COVID-19 prediction and symptom analysis using wearable sensors and IoT

Abstract: Purpose: The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances Design/methodology/approach: COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19 This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence The deployment of sensor devices has tremendously increased Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO2) Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test Findings: IoT devices today play a vital role in analyzing COVID-19 cases effectively The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information IoT devices contribute to tracking and monitoring the patients for prerequisites The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO2), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials Originality/value: The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server Few data are considered as outliers and discarded for analysis Only 208 data are contemplated for statistical examination These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206) © 2020, Emerald Publishing Limited

Challenges in successful implementation of Digital contact tracing to curb COVID-19 from global citizen’s perspective: A text analysis study

Abstract: Purpose: The word “digital contact tracing” is often met with different reactions: the reaction that passionately supports it, the reaction that neither supports nor oppose and the one that vehemently opposes it Those who support the notion of digital contact tracing vouch for its effectiveness and how the complicated process can be made simpler by implementing digital contact tracing, and those who oppose it often criticize the imminent threats it possesses However, without earning the support of a large population, it would be difficult for any government to implement digital contact tracing to perfection The purpose of this paper is to analyze, using machine learning, how different continents have different sentiments over digital contact tracing being used as a measure to curb COVID-19 Design/methodology/approach: For the analysis, data were collected from Twitter Tweets that contain the hashtag and the word “digital contact tracing” were crawled using Python library Tweepy Tweets across countries of four continents were collected from March 2020 to August 2020 In total, 70,212 tweets were used for this study Using the machine learning algorithm, the authors detected the sentiment of all the tweets belonging to each continent Structural topic modeling was used to understand the overall significant issues people voice out by global citizens while sharing their opinions on digital contact tracing Findings: This study was conducted in two parts Study one results show that North American and European citizens share more negative sentiments toward “digital contact tracing ” The citizens of the Asian and South American continent mostly share neutral sentiments regarding the digital contact tracing Overall, only 33% of total tweets were positively related to contact tracing, whereas 52% of the total tweets were neutral Study two results show that factors such as fear of government using contact tracing to spy on its people, the feeling of being unsafe and contact tracing being used to promote an agenda were the three major issues concerning the overall general public Originality/value: Despite numerous studies being conducted about how to implement the contact tracing efficiently, minimal studies were done to explore the possibility and challenges in implementing it This study fills the gap © 2020, Emerald Publishing Limited

Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors

Abstract: Purpose This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease Design/methodology/approach COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world Findings This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy The K-means algorithm has been applied for clustering the data set models For data collection, IoT devices and related sensors were used in the identified hotspots The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio This model is also tested with best, worst and average cases of data set to achieve the better prediction rate Originality/value From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802 11) to transfer the data to the authors' database, which is dedicated in data collection server The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19

Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems

Abstract: Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks.,To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration.,The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment.,Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0.

Multi-utility framework: blockchain exchange platform for sustainable development

Abstract: Purpose The purpose of this paper is to design a sustainable development platform for water and energy peer-to-peer trading that is financially and economically feasible. Water and other resources are becoming scarcer every day, and developing countries are the neediest for an immediate intervention. Water, as a national need, is considered to be one of the most precious commodities, but it is also one of the main causes for conflicts in the 21st century. Rainwater harvesting and peer-to-peer trading of the harvested water is one of the most convenient, scalable and sustainable solutions but faces organization challenges such as the absence of suitable business models motivating normal users to sell their generated resources (such as water and energy), currency and financial settlement complexities and single utility markets. Design/methodology/approach This paper proposes a multi-utility trading platform based on the blockchain technology which can address the challenges faced by peer-to-peer trading for resources such as energy and water. Findings This paper presents a peer-to-peer multi-utility trading platform that solves the shortcomings of existing utility frameworks reported in the current literature. Originality/value This proposed platform meets the needs of developing countries as well as rural areas of developed countries. The open nature of the proposed design makes it suitable for adoption and use by various stakeholders.